Text Generation
PEFT
Safetensors
English
qwen2
qlora
governed-agent
proposal-only
research-only
szl-holdings
khipu
abstain-retrain
conversational
Instructions to use SZLHOLDINGS/KHIPU-R2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SZLHOLDINGS/KHIPU-R2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "SZLHOLDINGS/KHIPU-R2") - Notebooks
- Google Colab
- Kaggle
Download merge_receipt.json from SZLHOLDINGS/KHIPU-R2: direct link, hf CLI and curl.
- Browser
- Download file 275 Bytes
-
https://huggingface.co/SZLHOLDINGS/KHIPU-R2/resolve/main/merge_receipt.json
- Command line
-
hf download hf://SZLHOLDINGS/KHIPU-R2/merge_receipt.json
-
curl -L -o merge_receipt.json https://huggingface.co/SZLHOLDINGS/KHIPU-R2/resolve/main/merge_receipt.json
275 Bytes
| { | |
| "repo": "SZLHOLDINGS/KHIPU-R2", | |
| "base_model": "Qwen/Qwen2.5-1.5B-Instruct", | |
| "base_revision": "main@merge-time", | |
| "merged_files": { | |
| "model.safetensors": "498f3f52d967d41ea75d556b35567472c392e30ef7d37c0925372f73d71754a3" | |
| }, | |
| "merge_dtype": "float32" | |
| } | |